Minority Report is a film about a police officer working in a department that can detect crimes before they occur, utilising Precogs—mutant beings plugged into machines that possess the ability to predict the future. For many years, humans have been intrigued by the possibility of computers and, in this case, artificial intelligence, being able to solve crimes. This fascination persists, especially considering that this movie was released 20 years ago.
But now, the use of AI technology is expected to increase in policing by 29% in 2024 globally, so the movie, way ahead of its time then, is already a reality. AI is used to predict criminals who will return to crime and over 60% of police in the United Kingdom (UK) use Machine Learning to predict crime. In fact, the AI market in the law enforcement sector is set to reach $1.65 billion by 2030.
But what exactly does AI do and how does it go about it? One way is to feed images of crime scenes so that machines learn how to detect clues and criminal patterns. After all, no one is unique and serial killers for example are known to repeat the same tactics over and over again to lure and kill victims. It can distinguish between particles left by individuals or pets, identify footwear from footprints, etc and look into all manner of minutiae to identify the criminal. AI also has the advantage of using data sets from police databases and can scour a lot of that information to give information that an individual investigator may not be able to do in a short time.
In addition to crime scenes, AI can be used in financial institutions to scan anomalous transactions through pattern recognition once trained to detect spurious activity on large data sets. AI can also create customer profiles based on behaviour and flag what is outside the norm. AI can also apply User Identification and 2-factor Authentication (2FA) which is exactly the same as what happens when you try to log in to your Gmail account from a machine it is unaccustomed to and you happen not to have your phone nearby. AI can also detect similar activity in unrelated accounts or detect if the same card is used both in Nairobi and New York in a relatively short period of time and other such tricks.
In Kenya, banking fraud has been on the rise, as indicated by the increasing number of court cases by customers. A number of banks have had to settle claims in court after being found negligent in handling customer accounts and money. Banking fraud in Kenya is multifaceted, manifesting in various guises such as identity theft, cyber security threats, fraudulent money transfers and the like. Banking fraud statistics in Kenya show that 6.8% of bank account holders lost money to customer fraud in 2021 though losses through mobile money users were much higher at 25.9%.
Against this growing tide, AI could be a fantastic solution but given its biases, there are valid concerns. For example, AI’s bias on race could predict more crimes by black men who have suffered from racial profiling in countries such as the US.
Despite the risks, American law enforcement agencies spend approximately $5 billion per year on AI and related technologies to support their work and it is time for Kenyan police and security firms to up their game and adopt AI. That might be a tall order though given that the police Occurrence Book was only digitised in June this year.
I am not sure, however, if that has been fully implemented because my phone was stolen recently and the occurrence was still recorded manually. I can attest to that by the hidden slip of paper that was torn off and given to me upon reporting. I would have preferred to just report the matter from my laptop as I had the phone IMEI number and the lady at the police station – she was not dressed like a police officer, more like an intern – was not very particular about how it was stolen, just the when. Information I could have quickly reported online. One can only hope that banks are faster at detecting and resolving crime so as to quickly resolve such matters without recourse to the courts which have clearly ruled that responsibility for the safety of customer money lies with the bank.